


"""simple docstring"""



import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


lowercase_							=							logging.get_logger(__name__)
def      UpperCAmelCase     (       _lowercase :   nn.ModuleList			,   _lowercase :   nn.ModuleList			,   _lowercase :   List[int]	)						-> None:


	"""simple docstring"""




	lowerCAmelCase_      							=			nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
	assert len(_lowercase	) == len(_lowercase	), F"""{len(_lowercase	)} != {len(_lowercase	)}"""
	dest_layers.load_state_dict(layers_to_copy.state_dict()	)


lowercase_							=							{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
lowercase_							=							{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def      UpperCAmelCase     (       _lowercase :   List[Any]			,   _lowercase :   List[str]	)						-> str:


	"""simple docstring"""




	try:
		lowerCAmelCase_      							=			LAYERS_TO_COPY[n_teacher][n_student]
		return val
	except KeyError:
		if n_student != n_teacher:
			warnings.warn(
			    F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
			    F""" {n_student}"""	)
		return list(range(_lowercase	)	)
def      UpperCAmelCase     (       _lowercase :   List[Any]			,   _lowercase :   Dict	)						-> List[int]:


	"""simple docstring"""




	if n_student > n_teacher:
		raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""	)
	elif n_teacher == n_student:
		return list(range(_lowercase	)	)
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def      UpperCAmelCase     (       _lowercase :   Union[str, PreTrainedModel]			,   _lowercase :   Union[str, Path] = "student"			,   _lowercase :   Union[int, None] = None			,   _lowercase :   Union[int, None] = None			,   _lowercase :   List[Any]=False			,   _lowercase :   Union[str, Any]=None			,   _lowercase :   Optional[int]=None			,   **_lowercase :   Any			,   )						-> Tuple[PreTrainedModel, List[int], List[int]]:


	"""simple docstring"""




	lowerCAmelCase_      							=			'''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
	assert (e is not None) or (d is not None), _msg
	if isinstance(_lowercase			,   _lowercase	):
		AutoTokenizer.from_pretrained(_lowercase	).save_pretrained(_lowercase	)  # purely for convenience
		lowerCAmelCase_      							=			AutoModelForSeqaSeqLM.from_pretrained(_lowercase	).eval()
	else:
		assert isinstance(_lowercase			,   _lowercase	), F"""teacher must be a model or string got type {type(_lowercase	)}"""
	lowerCAmelCase_      							=			teacher.config.to_diff_dict()

	try:
		lowerCAmelCase_							,	lowerCAmelCase_      							=			teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			lowerCAmelCase_      							=			teacher_e
		if d is None:
			lowerCAmelCase_      							=			teacher_d
		init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}	)
	except AttributeError:  # T5
		if hasattr(teacher.config			,   '''num_encoder_layers'''	):
			lowerCAmelCase_							,	lowerCAmelCase_      							=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			lowerCAmelCase_							,	lowerCAmelCase_      							=			teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			lowerCAmelCase_      							=			teacher_e
		if d is None:
			lowerCAmelCase_      							=			teacher_d
		if hasattr(teacher.config			,   '''num_encoder_layers'''	):
			init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}	)
		else:
			init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}	)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
	init_kwargs.update(_lowercase	)

	# Copy weights
	lowerCAmelCase_      							=			teacher.config_class(**_lowercase	)
	lowerCAmelCase_      							=			AutoModelForSeqaSeqLM.from_config(_lowercase	)
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	lowerCAmelCase_      							=			student.load_state_dict(teacher.state_dict()			,   strict=_lowercase	)
	assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

	if copy_first_teacher_layers:  # Our copying is done. We just log and save
		lowerCAmelCase_							,	lowerCAmelCase_      							=			list(range(_lowercase	)	), list(range(_lowercase	)	)
		logger.info(
		    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
		    F""" {save_path}"""	)
		student.save_pretrained(_lowercase	)
		return student, e_layers_to_copy, d_layers_to_copy

	# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
	if e_layers_to_copy is None:
		lowerCAmelCase_      							=			pick_layers_to_copy(_lowercase			,   _lowercase	)
	if d_layers_to_copy is None:
		lowerCAmelCase_      							=			pick_layers_to_copy(_lowercase			,   _lowercase	)

	try:
		if hasattr(
		    _lowercase			,   '''prophetnet'''	):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers			,   student.prophetnet.encoder.layers			,   _lowercase	)
			copy_layers(teacher.prophetnet.decoder.layers			,   student.prophetnet.decoder.layers			,   _lowercase	)
		else:
			copy_layers(teacher.model.encoder.layers			,   student.model.encoder.layers			,   _lowercase	)
			copy_layers(teacher.model.decoder.layers			,   student.model.decoder.layers			,   _lowercase	)
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block			,   student.encoder.block			,   _lowercase	)
		copy_layers(teacher.decoder.block			,   student.decoder.block			,   _lowercase	)
	logger.info(
	    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""	)
	lowerCAmelCase_      							=			{
	    '''teacher_type''': teacher.config.model_type,
	    '''copied_encoder_layers''': e_layers_to_copy,
	    '''copied_decoder_layers''': d_layers_to_copy,
	}
	student.save_pretrained(_lowercase	)
	# Save information about copying for easier reproducibility

	return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
		fire.Fire(create_student_by_copying_alternating_layers)



"""simple docstring"""



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_							=							16
lowercase_							=							32
def      UpperCAmelCase     (       _lowercase :   Accelerator			,   _lowercase :   int = 1_6			,   _lowercase :   str = "bert-base-cased"	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			AutoTokenizer.from_pretrained(_lowercase	)
	lowerCAmelCase_      							=			load_dataset('''glue'''			,   '''mrpc'''	)

	def tokenize_function(_lowercase :   Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase_      							=			tokenizer(examples['''sentence1''']			,   examples['''sentence2''']			,   truncation=_lowercase			,   max_length=_lowercase	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCAmelCase_      							=			datasets.map(
	    _lowercase			,   batched=_lowercase			,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,   load_from_cache_file=_lowercase	)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCAmelCase_      							=			tokenized_datasets.rename_column('''label'''			,   '''labels'''	)

	def collate_fn(_lowercase :   Any	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowercase			,   padding='''max_length'''			,   max_length=1_2_8			,   return_tensors='''pt'''	)
		return tokenizer.pad(_lowercase			,   padding='''longest'''			,   return_tensors='''pt'''	)

	# Instantiate dataloaders.
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''train''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''validation''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)

	return train_dataloader, eval_dataloader
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   Any			,   _lowercase :   Any			,   _lowercase :   str	)						-> List[Any]:


	"""simple docstring"""




	model.eval()
	lowerCAmelCase_      							=			0
	for step, batch in enumerate(_lowercase	):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device	)
		with torch.no_grad():
			lowerCAmelCase_      							=			model(**_lowercase	)
		lowerCAmelCase_      							=			outputs.logits.argmax(dim=-1	)
		# It is slightly faster to call this once, than multiple times
		lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.gather(
		    (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(_lowercase	) - 1:
				lowerCAmelCase_      							=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
				lowerCAmelCase_      							=			references[: len(eval_dataloader.dataset	) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=_lowercase			,   references=_lowercase			,   )

	lowerCAmelCase_      							=			metric.compute()
	return eval_metric["accuracy"]
def      UpperCAmelCase     (       _lowercase :   Dict			,   _lowercase :   Optional[Any]	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCAmelCase_      							=			config['''lr''']
	lowerCAmelCase_      							=			int(config['''num_epochs''']	)
	lowerCAmelCase_      							=			int(config['''seed''']	)
	lowerCAmelCase_      							=			int(config['''batch_size''']	)
	lowerCAmelCase_      							=			args.model_name_or_path

	set_seed(_lowercase	)
	lowerCAmelCase_							,	lowerCAmelCase_      							=			get_dataloaders(_lowercase			,   _lowercase			,   _lowercase	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase_      							=			AutoModelForSequenceClassification.from_pretrained(_lowercase			,   return_dict=_lowercase	)

	# Instantiate optimizer
	lowerCAmelCase_      							=			(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCAmelCase_      							=			optimizer_cls(params=model.parameters()			,   lr=_lowercase	)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCAmelCase_      							=			accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		lowerCAmelCase_      							=			1
	lowerCAmelCase_      							=			(len(_lowercase	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		lowerCAmelCase_      							=			get_linear_schedule_with_warmup(
		    optimizer=_lowercase			,   num_warmup_steps=0			,   num_training_steps=_lowercase			,   )
	else:
		lowerCAmelCase_      							=			DummyScheduler(_lowercase			,   total_num_steps=_lowercase			,   warmup_num_steps=0	)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.prepare(
	    _lowercase			,   _lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)

	# We need to keep track of how many total steps we have iterated over
	lowerCAmelCase_      							=			0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCAmelCase_      							=			0
	lowerCAmelCase_      							=			evaluate.load('''glue'''			,   '''mrpc'''	)
	lowerCAmelCase_      							=			num_epochs

	if args.partial_train_epoch is not None:
		lowerCAmelCase_      							=			args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint	)
		lowerCAmelCase_      							=			args.resume_from_checkpoint.split('''epoch_'''	)[1]
		lowerCAmelCase_      							=			''''''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		lowerCAmelCase_      							=			int(_lowercase	) + 1
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		accelerator.print('''resumed checkpoint performance:'''			,   _lowercase	)
		accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			,   lr_scheduler.get_lr()[0]	)
		accelerator.print('''resumed optimizers\'s lr:'''			,   optimizer.param_groups[0]['''lr''']	)
		with open(os.path.join(args.output_dir			,   F"""state_{starting_epoch-1}.json"""	)			,   '''r'''	) as f:
			lowerCAmelCase_      							=			json.load(_lowercase	)
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	lowerCAmelCase_      							=			{}
	for epoch in range(_lowercase			,   _lowercase	):
		model.train()
		for step, batch in enumerate(_lowercase	):
			lowerCAmelCase_      							=			model(**_lowercase	)
			lowerCAmelCase_      							=			outputs.loss
			lowerCAmelCase_      							=			loss / gradient_accumulation_steps
			accelerator.backward(_lowercase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		lowerCAmelCase_      							=			F"""epoch_{epoch}"""
		lowerCAmelCase_      							=			os.path.join(args.output_dir			,   _lowercase	)
		accelerator.save_state(_lowercase	)
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		lowerCAmelCase_      							=			accuracy
		lowerCAmelCase_      							=			lr_scheduler.get_lr()[0]
		lowerCAmelCase_      							=			optimizer.param_groups[0]['''lr''']
		lowerCAmelCase_      							=			epoch
		lowerCAmelCase_      							=			overall_step
		accelerator.print(F"""epoch {epoch}:"""			,   _lowercase	)

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir			,   F"""state_{epoch}.json"""	)			,   '''w'''	) as f:
				json.dump(_lowercase			,   _lowercase	)
def      UpperCAmelCase     (       )						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
	parser.add_argument(
	    '''--model_name_or_path'''			,   type=_lowercase			,   default='''bert-base-cased'''			,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,   required=_lowercase			,   )
	parser.add_argument(
	    '''--output_dir'''			,   type=_lowercase			,   default='''.'''			,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,   )
	parser.add_argument(
	    '''--resume_from_checkpoint'''			,   type=_lowercase			,   default=_lowercase			,   help='''If the training should continue from a checkpoint folder.'''			,   )
	parser.add_argument(
	    '''--partial_train_epoch'''			,   type=_lowercase			,   default=_lowercase			,   help='''If passed, the training will stop after this number of epochs.'''			,   )
	parser.add_argument(
	    '''--num_epochs'''			,   type=_lowercase			,   default=2			,   help='''Number of train epochs.'''			,   )
	lowerCAmelCase_      							=			parser.parse_args()
	lowerCAmelCase_      							=			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

	training_function(_lowercase			,   _lowercase	)


if __name__ == "__main__":
		main()